Using Trust for Heterogeneous Human-Robot Team Task Allocation

TL;DR

This paper reviews how trust influences task allocation in human-robot teams and proposes a bi-directional trust model to improve task assignment strategies, aiming to enhance team performance.

Contribution

It introduces a bi-directional trust model for task allocation that predicts trust levels and integrates them into decision-making, addressing a gap in existing strategies.

Findings

Trust-based task allocation can improve team performance.

The bi-directional trust model predicts trust for new and existing tasks.

Future work includes incorporating human trust and negotiation mechanisms.

Abstract

Human-robot teams have the ability to perform better across various tasks than human-only and robot-only teams. However, such improvements cannot be realized without proper task allocation. Trust is an important factor in teaming relationships, and can be used in the task allocation strategy. Despite the importance, most existing task allocation strategies do not incorporate trust. This paper reviews select studies on trust and task allocation. We also summarize and discuss how a bi-directional trust model can be used for a task allocation strategy. The bi-directional trust model represents task requirements and agents by their capabilities, and can be used to predict trust for both existing and new tasks. Our task allocation approach uses predicted trust in the agent and expected total reward for task assignment. Finally, we present some directions for future work, including the incorporation of trust from the human and human capacity for task allocation, and a negotiation phase for resolving task disagreements.